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Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks

Jul 10, 2020
Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Liu, Jasper Snoek, Balaji Lakshminarayanan

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Interpreting and Disentangling Feature Components of Various Complexity from DNNs

Jun 29, 2020
Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang

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SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders

Oct 02, 2019
Peter J. Liu, Yu-An Chung, Jie Ren

* first two authors contributed equally 

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Likelihood Ratios for Out-of-Distribution Detection

Jun 07, 2019
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan

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Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Jun 06, 2019
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek

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Explaining Neural Networks Semantically and Quantitatively

Dec 18, 2018
Runjin Chen, Hao Chen, Ge Huang, Jie Ren, Quanshi Zhang

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To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference

Oct 21, 2018
Qing Qin, Jie Ren, Jialong Yu, Ling Gao, Hai Wang, Jie Zheng, Yansong Feng, Jianbin Fang, Zheng Wang

* 8 pages, To appear in ISPA 2018 

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Active Learning for Wireless IoT Intrusion Detection

Aug 04, 2018
Kai Yang, Jie Ren, Yanqiao Zhu, Weiyi Zhang

* 7 pages, 4 figures, accepted by IEEE Wireless Communications 

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An End-to-End Compression Framework Based on Convolutional Neural Networks

Aug 02, 2017
Feng Jiang, Wen Tao, Shaohui Liu, Jie Ren, Xun Guo, Debin Zhao

* Submitted to IEEE Transactions on Circuits and Systems for Video Technology 

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